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Using a hybrid approach for wind power forecasting in Northwestern Mexico
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Statistical post‐processing of turbulence‐resolving weather forecasts for offshore wind power forecasting
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The future of forecasting for renewable energy
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Wind power forecasting—a review of the state of the art
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Support vector regression
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Skill forecasting from ensemble predictions of wind power
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Wind speed and wind energy forecast through Kalman filtering of Numerical Weather Prediction model output
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A novel bidirectional mechanism based on time series model for wind power forecasting
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Multi-step wind speed and power forecasts based on a WRF simulation and an optimized association method
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A novel two-stage forecasting model based on error factor and ensemble method for multi-step wind power forecasting
inferredcites →https://doi.org/10.1016/j.compfluid.2013.04.029openalex-snapshot
CFD simulations of turbulent buoyant atmospheric flows over complex geometry: Solver development in OpenFOAM
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Wind power forecast using wavelet neural network trained by improved Clonal selection algorithm
inferredcites →https://doi.org/10.1016/j.enconman.2016.06.053openalex-snapshot
Current status of wind energy forecasting and a hybrid method for hourly predictions
inferredcites →https://doi.org/10.1016/j.enconman.2016.09.002openalex-snapshot
A hybrid wind power forecasting model based on data mining and wavelets analysis
inferredcites →https://doi.org/10.1016/j.energy.2017.04.094openalex-snapshot
Wind power prediction using hybrid autoregressive fractionally integrated moving average and least square support vector machine
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Hourly day-ahead wind power forecasting at two wind farms in northeast Brazil using WRF model
inferredcites →https://doi.org/10.1016/j.jhydrol.2014.01.062openalex-snapshot
Monthly streamflow prediction using modified EMD-based support vector machine
inferredcites →https://doi.org/10.1016/j.jweia.2008.03.013openalex-snapshot
Improvements in wind speed forecasts for wind power prediction purposes using Kalman filtering
inferredcites →https://doi.org/10.1016/j.jweia.2014.08.007openalex-snapshot
Investigation of model parameters for high-resolution wind energy forecasting: Case studies over simple and complex terrain
inferredcites →https://doi.org/10.1016/j.ref.2019.03.001openalex-snapshot
Wind resource assessment and sensitivity analysis of the levelised cost of energy. A case study in Tabasco, Mexico
inferredcites →https://doi.org/10.1016/j.renene.2003.11.009openalex-snapshot
Support vector machines for wind speed prediction
inferredcites →https://doi.org/10.1016/j.renene.2006.10.005openalex-snapshot
Wind speed forecasting in the South Coast of Oaxaca, México
inferredcites →https://doi.org/10.1016/j.renene.2008.03.014openalex-snapshot
Short term wind speed forecasting in La Venta, Oaxaca, México, using artificial neural networks
inferredcites →https://doi.org/10.1016/j.renene.2009.10.037openalex-snapshot
Analysis and forecasting of wind velocity in chetumal, quintana roo, using the single exponential smoothing method
inferredcites →https://doi.org/10.1016/j.renene.2010.04.022openalex-snapshot
Wind speed forecasting in three different regions of Mexico, using a hybrid ARIMA–ANN model
inferredcites →https://doi.org/10.1016/j.renene.2011.05.033openalex-snapshot
Current methods and advances in forecasting of wind power generation
inferredcites →https://doi.org/10.1016/j.renene.2014.09.058openalex-snapshot
Short-term wind power forecasting using adaptive neuro-fuzzy inference system combined with evolutionary particle swarm optimization, wavelet transform and mutual information
inferredcites →https://doi.org/10.1016/j.renene.2014.11.061openalex-snapshot
A novel application of an analog ensemble for short-term wind power forecasting
inferredcites →https://doi.org/10.1016/j.renene.2015.07.004openalex-snapshot
Wind speed forecasting for wind farms: A method based on support vector regression
inferredcites →https://doi.org/10.1016/j.renene.2015.11.073openalex-snapshot
Machine learning ensembles for wind power prediction
inferredcites →https://doi.org/10.1016/j.renene.2016.10.074openalex-snapshot
A novel hybrid methodology for short-term wind power forecasting based on adaptive neuro-fuzzy inference system
inferredcites →https://doi.org/10.1016/j.renene.2016.12.071openalex-snapshot
An improved neural network-based approach for short-term wind speed and power forecast
inferredcites →https://doi.org/10.1016/j.renene.2017.10.111openalex-snapshot
Short term wind power forecasting using hybrid variational mode decomposition and multi-kernel regularized pseudo inverse neural network
inferredcites →https://doi.org/10.1016/j.renene.2018.12.047openalex-snapshot
Wind power forecasting for a real onshore wind farm on complex terrain using WRF high resolution simulations
inferredcites →https://doi.org/10.1016/j.renene.2022.06.139openalex-snapshot
Wind speed forecasting using a hybrid model considering the turbulence of the airflow
inferredcites →https://doi.org/10.1016/j.renene.2022.11.042openalex-snapshot
A hierarchical classification/regression algorithm for improving extreme wind speed events prediction
inferredcites →https://doi.org/10.1016/j.rse.2005.05.008openalex-snapshot
Decision tree regression for soft classification of remote sensing data
inferredcites →https://doi.org/10.1016/j.rser.2014.03.033openalex-snapshot
A review of combined approaches for prediction of short-term wind speed and power
inferredcites →https://doi.org/10.1016/j.rser.2020.110515openalex-snapshot
An overview of performance evaluation metrics for short-term statistical wind power forecasting
inferredcites →https://doi.org/10.1063/1.4940208openalex-snapshot
A wind power forecasting system based on the weather research and forecasting model and Kalman filtering over a wind-farm in Japan
inferredcites →https://doi.org/10.1063/1.4966693openalex-snapshot
An integrated wind-forecast system based on the weather research and forecasting model, Kalman filter, and data assimilation with nacelle-wind observation
inferredcites →https://doi.org/10.1080/19648189.2017.1304273openalex-snapshot
Efficiency of using 4DVar, 3DVar and EnKF data assimilation methods in groundwater contaminant transport modelling
inferredcites →https://doi.org/10.1109/icpes.2016.7584025openalex-snapshot
Separate wind power and ramp predictions based on meteorological variables and clustering method
inferredcites →https://doi.org/10.1109/td-asia.2009.5356831openalex-snapshot
Application of NBTree to selection of meteorological variables in wind speed prediction
inferredcites →https://doi.org/10.1109/tec.2003.821865openalex-snapshot
A Fuzzy Model for Wind Speed Prediction and Power Generation in Wind Parks Using Spatial Correlation
inferredcites →https://doi.org/10.1109/tec.2008.2001457openalex-snapshot
Forecasting the Wind Generation Using a Two-Stage Network Based on Meteorological Information
inferredcites →https://doi.org/10.1109/tpwrs.2011.2160295openalex-snapshot
A Method for Short-Term Wind Power Prediction With Multiple Observation Points
inferredcites →https://doi.org/10.1109/tsg.2015.2424078openalex-snapshot
Very-Short-Term Probabilistic Wind Power Forecasts by Sparse Vector Autoregression
inferredcites →https://doi.org/10.1109/tste.2017.2694340openalex-snapshot
Improving Renewable Energy Forecasting With a Grid of Numerical Weather Predictions
inferredcites →https://doi.org/10.1175/waf-d-13-00066.1openalex-snapshot
Precipitation and Temperature Forecast Performance at the Weather Prediction Center
inferredcites →https://doi.org/10.13140/rg.2.1.2310.6645openalex-snapshot
A Description of the Advanced Research WRF Version 3
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A Comprehensive Wind Power Forecasting System Integrating Artificial Intelligence and Numerical Weather Prediction
inferredcites →https://doi.org/10.3390/fluids5040225openalex-snapshot
PyDA: A Hands-On Introduction to Dynamical Data Assimilation with Python
inferredcites →https://doi.org/10.3390/rs10111701openalex-snapshot
On the Use of Dual-Doppler Radar Measurements for Very Short-Term Wind Power Forecasts
inferredcites →https://doi.org/10.48550/arxiv.1201.0490openalex-snapshot
Scikit-learn: Machine Learning in Python
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NCEP FNL Operational Model Global Tropospheric Analyses, continuing from July 1999
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.2139/ssrn.4772293openalex-extracted
Atmospheric Mesoscale Modeling to Simulate Annual and Seasonal Wind Speeds for Wind Energy Production in Mexico
inferred← citeshttps://doi.org/10.1007/s41660-026-00730-zopenalex-snapshot
A Comprehensive Review of AI-based Wind Power Forecasting Over Multiple Time Horizons
inferred← citeshttps://doi.org/10.1016/j.seta.2024.103848openalex-snapshot
Atmospheric mesoscale modeling to simulate annual and seasonal wind speeds for wind energy production in Mexico
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